Distinctiveness Centrality in Social Networks

The determination of node centrality is a fundamental topic in social network studies. As an addition to established metrics, which identify central nodes based on their brokerage power, the number and weight of their connections, and the ability to quickly reach all other nodes, we introduce five new measures of Distinctiveness Centrality. These new metrics attribute a higher score to nodes keeping a connection with the network periphery. They penalize links to highly-connected nodes and serve the identification of social actors with more distinctive network ties. We discuss some possible applications and properties of these newly introduced metrics, such as their upper and lower bounds. Distinctiveness centrality provides a viewpoint of centrality alternative to that of established metrics

Hints to forecast macroeconomic indicators

We propose a novel method to improve the forecast of macroeconomic indicators based on social network and semantic analysis techniques. In particular, we explore variables extracted from the Global Database of Events, Language, and Tone, which monitors the world's broadcast, print and web news. We investigate the locations and the countries involved in economic events (such as business or economic agreements), as well as the tone and the Goldstein scale of the news where the events are reported. We connect these elements to build three different social networks and to extract new network metrics, which prove their value in extending the predictive power of models only based on the inclusion of other economic or demographic indices. We find that the number of news, their tone, the network constraint of nations and their betweenness centrality oscillations are important predictors of the Gross Domestic Product per Capita and of the Business and Consumer Confidence indices

The identity of social impact venture capitalists: exploring social linguistic positioning and linguistic distinctiveness through text mining

Impact investing is gaining momentum as an investment practice that optimizes both financial and social outcomes. However, the market is still in its emerging stage, and there is ambiguity regarding the definition of players and practices. In this paper, we adopt an investor identity perspective and use a linguistic approach to explore how social impact venture capitalists (SIVCs) communicate their identities and actions to their external stakeholders. Through a text mining analysis of the websites of 195 investors worldwide, our results reveal four types of investors who differ in terms of their social linguistic positioning and linguistic distinctiveness. Finally, by training a tree boosting machine learning model, we assess the extent to which the use of different linguistic styles is associated with website traffic

Exploiting the Potential Value of Over-the-Counter Drugs through Brand Equity: An Analytic Network Process Approach

We propose a network model to identify the main drivers of consumer-based brand equity. We apply our research to assess the value of three over-the-counter drug brands. Our aim is to help manufacturers to improve their position in the market of self-medication. This market has very peculiar characteristics: consumers buy products in response to their specific health needs; nonetheless, the market is not strictly regulated in the same way that the prescription market, which allows firms to choose their pricing and communication strategies. Moreover, consumers are not forced by physicians to buy one specific drug, but they can choose the one they prefer. To develop our model we use the Analytic Network Process methodology, which allows integrating qualitative and quantitative judgments from many decision makers and deals with non-regular preference structures. The output of the model is a ranking of the brand value drivers, according to their importance in influencing the consumers' purchase intentions. We find that advertising plays a major role in this setting. To test our model and validate our results, we analyse three Italian brands that produce over-the-counter (OTC) Diclofenac-based drugs. In addition, we compare our results with their market share

quality management in the design of tlc call centers

immediate Abstract Call centres rely heavily on the self-service paradigm through the use of an automated IVR (Interactive Voice Response) system. The service time delivered by the IVR is a major component of the overall QoS (Quality of Service) delivered by the call centre. We analyse the structure and service times of IVR systems through a case study of five call centres in the telecommunications sector. The service trees of the call centres under survey are reconstructed by complete exploration and analysed through a set of metrics. The present design of service trees leads to service times typically larger than those spent waiting for a human agent and to excessively long announcements, with a negative impact on the overall QoS. Imbalances in the popularity of the services offered by the IVR can be exploited to reduce remarkably the average service time, by properly matching the most popular services with the shortest service times

The language and social behavior of innovators

Innovators are creative people who can conjure the ground-breaking ideas that represent the main engine of innovative organizations. Past research has extensively investigated who innovators are and how they behave in work-related activities. In this paper, we suggest that it is necessary to analyze how innovators behave in other contexts, such as in informal communication spaces, where knowledge is shared without formal structure, rules, and work obligations. Drawing on communication and network theory, we analyze about 38,000 posts available in the intranet forum of a large multinational company. From this, we explain how innovators differ from other employees in terms of social network behavior and language characteristics. Through text mining, we find that innovators write more, use a more complex language, introduce new concepts/ideas, and use positive but factual-based language. Understanding how innovators behave and communicate can support the decision-making processes of managers who want to foster innovation

Brand Network Booster: A New System for Improving Brand Connectivity

This paper presents a new decision support system offered for an in-depth analysis of semantic networks, which can provide insights for a better exploration of a brand's image and the improvement of its connectivity. In terms of network analysis, we show that this goal is achieved by solving an extended version of the Maximum Betweenness Improvement problem, which includes the possibility of considering adversarial nodes, constrained budgets, and weighted networks - where connectivity improvement can be obtained by adding links or increasing the weight of existing connections. We present this new system together with two case studies, also discussing its performance. Our tool and approach are useful both for network scholars and for supporting the strategic decision-making processes of marketing and communication managers

Evaluating and improving social awareness of energy communities through semantic network analysis of online news

The implementation of energy communities represents a cross-disciplinary phenomenon that has the potential to support the energy transition while fostering citizens' participation throughout the energy system and their exploitation of renewables. An important role is played by online information sources in engaging people in this process and increasing their awareness of associated benefits. In this view, this work analyses online news data on energy communities to understand people's awareness and the media importance of this topic. We use the Semantic Brand Score (SBS) indicator as an innovative measure of semantic importance, combining social network analysis and text mining methods. Results show different importance trends for energy communities and other energy and society-related topics, also allowing the identification of their connections. Our approach gives evidence to information gaps and possible actions that could be taken to promote a low-carbon energy transition

Analytic Hierarchy Process for New Product Development

The success of a New Product Development (NPD) process strongly depends on the deep comprehension of market needs and Analytic Hierarchy Process (AHP) has been commonly used to find weights for customers’ preferences. AHP best practices suggest that low‐consistency respondents should be considered untrustworthy; however, in some NPD cases – such as the one presented here – this stake can be extremely big. This paper deals with the usage of AHP methodology to define the weights of customer needs connected to the NPD process of a typical impulse buying good, a snack. The aim of the paper is to analyse in a critical way the opportunity to exclude or include non‐consistent respondents in market analysis, addressing the following question: should a non‐consistent potential customer be excluded from the analysis due to his inconsistency or should he be included because, after all, he is still a potential consumer? The chosen methodological approach focuses on evaluating the compatibility of weight vectors among different subsets of respondents, filtered according to their consistency level. Results surprisingly show that weights do not significantly change when non‐consistent respondents are excluded